Automatic analysis of real time conditions in an activity space

ABSTRACT

Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success. The analyzing can include automatic identification of potential collision points for a first actor, including potential collision points with the newly discovered object. The newly discovered previously unmodeled entity interferes with an actor from performing an activity. The newly discovered object is a portion of a tool component of a product.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/581,541 filed Nov. 3, 2017, which isincorporated herein in its entirety.

BACKGROUND

Activities performed in various work environments have made significantcontributions toward the advancement of society and to the realizationof numerous advantageous results. The manner in which the actors performthe activities typically has significant impact on successfullyachieving various objectives associated with the activities. Theobjectives can include correctly performing an action or task (e.g.,assembling product components, interacting with another actor/entity,etc.) effectively and efficiently (e.g., low cost, timely/rapidly,conservation of resources and energy, safely, repeatability, etc.).However, reliable and proper achievement of the various objectives canbe difficult.

Numerous factors can impact successful achievement of the objectives.Realization of the objectives can be influenced by the work environment(e.g., manufacturing environments, service environments, medicalenvironments, retail environments, etc.), the nature of the activityitself (e.g., complex, simple, repetitive, non-uniform, safe. hazardous,etc.), and business considerations (e.g., cost, market responsiveness,transportation. etc.). Proper performance of the activities oftendepends upon the various aspects of actors involved in performing anactivity. There can be different actors. An actor can be human, aprogrammable machine (e.g., a robot, a cobot, a CNC machine, etc.), anda hard automation machine (e.g., a casting machine,______, etc.). Theactors in turn can have various characteristics, attributes, andfeatures. For example, human actors can have intelligence, cognition,instinct, reflex, intuition and so on, but can also be prone toinconsistency, physical limitations, injury and other human limitations.A robot can have precision, repeatability, is untiring, hardy and so on,but lack intuition, intelligence, initiative, adaptability, and soforth. In one embodiment, a programmable machine is relatively difficultand time consuming to program/reprogram and a human is capable oflearning and understanding relatively easily and quickly.

Given variances in actor characteristics and capabilities, coordinationof actors and activities can have a significant impact on realization ofan objective. The types of actors and activities can impact a station orworkspace configuration (e.g., layout, space and volume occupied,tooling, etc.), a process output (e.g., product quality, etc.), costs(e.g., costs associated with energy utilization, initial investment,labor, tooling, etc.). Conventional attempts at dealing with thesenumerous complex issues are typically costly and resource intensive, andefficient and effective coordination of actors and activities istraditionally often very problematic or impossible.

SUMMARY

Efficient and effective workspace condition analysis systems and methodsare presented. In one embodiment, a method comprises: accessinginformation associated with an activity space, including information ona newly discovered previously unmodeled entity; analyzing the activityinformation, including activity information associated with thepreviously unmodeled entity; forwarding feedback on the results of theanalysis, including analysis results for the updated modeledinformation; and utilizing the feedback in a coordinated path plan checkprocess. In one exemplary implementation the coordinated path plan checkprocess comprises: creating a solid/CAD model including updated modeledinformation; simulating an activity including the updated modeledinformation; generating a coordinated path plan for entities in theactivity space; and testing the coordinated path plan. The coordinatedpath plan check process can be a success. The analyzing can includeautomatic identification of potential collision points for a firstactor, including potential collision points with the newly discoveredobject. The newly discovered previously unmodeled entity can interferewith an actor from performing an activity. The newly discovered objectcan be a portion of a tool component of a product, a robot, and so on.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present technology are illustrated by way of exampleand not by way of limitation, in the figures of the accompanyingdrawings and in which like reference numerals refer to similar elements.

FIG. 1 shows an action recognition and analytics system, in accordancewith aspect of the present technology.

FIG. 2 shows an exemplary deep learning type machine learning back-endunit, in accordance with aspects of the present technology.

FIG. 3 shows an exemplary Convolution Neural Network (CNN) and LongShort Term Memory (LSTM) Recurrent Neural Network (RNN), in accordancewith aspects of the present technology.

FIG. 4 shows an exemplary method of detecting actions in a sensorstream, in accordance with aspects of the present technology.

FIG. 5 shows an action recognition and analytics system, in accordancewith aspects of the present technology.

FIG. 6 shows an exemplary method of detecting actions, in accordancewith aspects of the present technology.

FIG. 7 shows an action recognition and analytics system, in accordancewith aspects of the present technology.

FIG. 8 shows an exemplary station, in accordance with aspects of thepresent technology

FIG. 9 shows an exemplary station, in accordance with aspects of thepresent technology

FIG. 10 shows an exemplary station activity analysis method, inaccordance with one embodiment.

FIG. 11 is a block diagram of an exemplary activity space with an actorin accordance with one embodiment.

FIG. 12 is a block diagram of an exemplary activity space with an actorin accordance with one embodiment.

FIG. 13 is a flow chart of an exemplary automated actor modeling methodin accordance with one embodiment.

FIG. 14, a side view of an exemplary reach scenario in a station isshown in accordance with one embodiment.

FIG. 15 is a top down view of another exemplary reach scenario in astation is shown in accordance with one embodiment.

FIG. 16 is a block diagram of an exemplary data structure in accordancewith one embodiment.

FIG. 17 is an illustration of an exemplary convex hull curve of thereach points in accordance with one embodiment.

FIG. 18 is an exemplary reach curve with human motion dynamics inaccordance with one embodiment.

FIG. 19 is an exemplary reach curve with attitudinal control inaccordance with one embodiment.

FIG. 20 is an exemplary maximum reach curve associated with requirementsof different activities in accordance with one embodiment.

FIG. 21 is a flow chart of exemplary method in accordance with oneembodiment.

FIG. 22 is a flow chart of a exemplary coordinated plan check process inaccordance with one embodiment.

FIG. 23 is a block diagram of an exemplary activity area and flow chartof a activity modeling method in accordance with one embodiment.

FIG. 24 is a block diagram of an exemplary activity area and flow chartof a activity modeling method in accordance with one embodiment.

FIG. 25 is a block diagram of an exemplary activity area and flow chartof a activity modeling method in accordance with one embodiment.

FIG. 26 is a block diagram of a exemplary data structure 2 in accordancewith one embodiment.

FIG. 27 is a block diagram of a exemplary data structure 2700 inaccordance with one embodiment.

FIG. 28 is a flow chart of a exemplary method 2800 in accordance withone embodiment.

FIG. 29 shows a block diagram of an example of a computing system 2900upon which one or more various embodiments described herein may beimplemented in accordance with various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. While the invention will be described in conjunction with thepreferred embodiments, it will be understood that they are not intendedto limit the invention to these embodiments. On the contrary, theinvention is intended to cover alternatives, modifications andequivalents, which may be included within the spirit and scope of theinvention as defined by the appended claims. Furthermore, in thefollowing detailed description of the present invention, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. However, it will be obvious toone ordinarily skilled in the art that the present invention may bepracticed without these specific details. In other instances, well knownmethods, procedures, components, and circuits have not been described indetail as not to unnecessarily obscure aspects of the current invention.

As used herein the term process can include processes, procedures,transactions, routines, practices, and the like. As used herein the termsequence can include sequences, orders, arrangements, and the like. Asused herein the term action can include actions, steps, tasks, activity,motion, movement, and the like. As used herein the term object caninclude objects, parts, components, items, elements, pieces, assemblies,sub-assemblies, and the like. As used herein a process can include a setof actions or one or more subsets of actions, arranged in one or moresequences, and performed on one or more objects by one or more actors.As used herein a cycle can include a set of processes or one or moresubsets of processes performed in one or more sequences. As used hereina sensor stream can include a video sensor stream, thermal sensorstream, infrared sensor stream, hyperspectral sensor stream, audiosensor stream, depth data stream, and the like. As used herein framebased sensor stream can include any sensor stream that can berepresented by a two or more dimensional array of data values. As usedherein the term parameter can include parameters, attributes, or thelike. As used herein the term indicator can include indicators,identifiers, labels, tags, states, attributes, values or the like. Asused herein the term feedback can include feedback, commands,directions, alerts, alarms, instructions, orders, and the like. As usedherein the term actor can include actors, workers, employees, operators,assemblers, contractors, associates, managers, users, entities, humans,cobots, robots, and the like as well as combinations of them. As usedherein the term robot can include a machine, device, apparatus or thelike, especially one programmable by a computer, capable of carrying outa series of actions automatically. The actions can be autonomous,semi-autonomous, assisted, or the like. As used herein the term cobotcan include a robot intended to interact with humans in a sharedworkspace. As used herein the term package can include packages,packets, bundles, boxes, containers, cases, cartons, kits, and the like.As used herein, real time can include responses within a given latency,which can vary from sub-second to seconds

Referring to FIG. 1 an action recognition and analytics system, inaccordance with aspect of the present technology, is shown. The actionrecognition and analytics system 100 can be deployed in a manufacturing,health care, warehousing, shipping, retail, restaurant or similarcontext. A manufacturing context, for example, can include one or morestations 105-115 and one or more actors 120-130 disposed at the one ormore stations. The actors can include humans, machine or any combinationtherefore. For example, individual or multiple workers can be deployedat one or more stations along a manufacturing assembly line. One or morerobots can be deployed at other stations. A combination of one or moreworkers and/or one or more robots can be deployed additional stations Itis to be noted that the one or more stations 105-115 and the one or moreactors are not generally considered to be included in the system 100.

In a health care implementation, an operating room can comprise a singlestation implementation. A plurality of sensors, such as video cameras,thermal imaging sensors, depth sensors, or the like, can be disposednon-intrusively at various positions around the operating room. One ormore additional sensors, such as audio, temperature, acceleration,torque, compression, tension, or the like sensors, can also be disposednon-intrusively at various positions around the operating room.

In a shipping implementation, the plurality of stations may representdifferent loading docks, conveyor belts, forklifts, sorting stations,holding areas, and the like. A plurality of sensors, such as videocameras, thermal imaging sensors, depth sensors, or the like, can bedisposed non-intrusively at various positions around the loading docks,conveyor belts, forklifts, sorting stations, holding areas, and thelike. One or more additional sensors, such as audio, temperature,acceleration, torque, compression, tension, or the like sensors, canalso be disposed non-intrusively at various positions.

In a retailing implementation, the plurality of stations may representone or more loading docks, one or more stock rooms, the store shelves,the point of sale (e.g. cashier stands, self-checkout stands andauto-payment geofence), and the like. A plurality of sensors such asvideo cameras, thermal imaging sensors, depth sensors, or the like, canbe disposed non-intrusively at various positions around the loadingdocks, stock rooms, store shelves, point of sale stands and the like.One or more additional sensors, such as audio, acceleration, torque,compression, tension, or the like sensors, can also be disposednon-intrusively at various positions around the loading docks, stockrooms, store shelves, point of sale stands and the like.

In a warehousing or online retailing implementation, the plurality ofstations may represent receiving areas, inventory storage, pickingtotes, conveyors, packing areas, shipping areas, and the like. Aplurality of sensors, such as video cameras, thermal imaging sensors,depth sensors, or the like, can be disposed non-intrusively at variouspositions around the receiving areas, inventory storage, picking totes,conveyors, packing areas, and shipping areas. One or more additionalsensors, such as audio, temperature, acceleration, torque, compression,tension, or the like sensors, can also be disposed non-intrusively atvarious positions.

Aspect of the present technology will be herein further described withreference to a manufacturing context so as to best explain theprinciples of the present technology without obscuring aspects of thepresent technology. However, the present technology as further describedbelow can also be readily applied in health care, warehousing, shipping,retail, restaurants, and numerous other similar contexts.

The action recognition and analytics system 100 can include one or moreinterfaces 135-165. The one or more interface 135-145 can include one ormore sensors 135-145 disposed at the one or more stations 105-115 andconfigured to capture streams of data concerning cycles, processes,actions, sequences, object, parameters and or the like by the one ormore actors 120-130 and or at the station 105-115. The one or moresensors 135-145 can be disposed non-intrusively, so that minimal tochanges to the layout of the assembly line or the plant are required, atvarious positions around one or more of the stations 105-115. The sameset of one or more sensors 135-145 can be disposed at each station105-115, or different sets of one or more sensors 135-145 can bedisposed at different stations 105-115. The sensors 135-145 can includeone or more sensors such as video cameras, thermal imaging sensors,depth sensors, or the like. The one or more sensors 135-145 can alsoinclude one or more other sensors, such as audio, temperature,acceleration, torque, compression, tension, or the like sensors.

The one or more interfaces 135-165 can also include but not limited toone or more displays, touch screens, touch pads, keyboards, pointingdevices, button, switches, control panels, actuators, indicator lights,speakers, Augmented Reality (AR) interfaces, Virtual Reality (VR)interfaces, desktop Personal Computers (PCs), laptop PCs, tablet PCs,smart phones, robot interfaces, cobot interfaces. The one or moreinterfaces 135-165 can be configured to receive inputs from one or moreactors 120-130, one or more engines 170 or other entities. Similarly,the one or more interfaces 135-165 can be configured to output to one ormore actors 120-130, one or more engine 170 or other entities. Forexample, the one or more front-end units 190 can output one or moregraphical user interfaces to present training content, work charts, realtime alerts, feedback and or the like on one or more interfaces 165,such displays at one or more stations 120-130, at management portals ontablet PCs, administrator portals as desktop PCs or the like. In anotherexample, the one or more front-end units 190 can control an actuator topush a defective unit of the assembly line when a defect is detected.The one or more front-end units can also receive responses on a touchscreen display device, keyboard, one or more buttons, microphone or thelike from one or more actors. Accordingly, the interfaces 135-165 canimplement an analysis interface, mentoring interface and or the like ofthe one or more front-end units 190.

The action recognition and analytics system 100 can also include one ormore engines 170 and one or more data storage units 175. The one or moreinterfaces 135-165, the one or more data storage units 175, the one ormore machine learning back-end units 180, the one or more analyticsunits 185, and the one or more front-end units 190 can be coupledtogether by one or more networks 192. It is also to be noted thatalthough the above described elements are described as separateelements, one or more elements of the action recognition and analyticssystem 100 can be combined together or further broken into differentelements.

The one or more engines 170 can include one or more machine learningback-end units 180, one or more analytics units 185, and one or morefront-end units 190. The one or more data storage units 175, the one ormore machine learning back-end units 180, the one or more analyticsunits 185, and the one or more analytics front-end units 190 can beimplemented on a single computing device, a common set of computingdevices, separate computing device, or different sets of computingdevices that can be distributed across the globe inside and outside anenterprise. Aspects of the one or more machine learning back-end units180, the one or more analytics units 185 and the one or more front-endunits 190, and or other computing units of the action recognition andanalytics system 100 can be implemented by one or more centralprocessing units (CPU), one or more graphics processing units (GPU), oneor more tensor processing units (TPU), one or more digital signalprocessors (DSP), one or more microcontrollers, one or more fieldprogrammable gate arrays and or the like, and any combination thereof.In addition, the one or more data storage units 175, the one or moremachine learning back-end units 180, the one or more analytics units185, and the one or more front-end units 190 can be implemented locallyto the one or more stations 105-115, remotely from the one or morestations 105-115, or any combination of locally and remotely. In oneexample, the one or more data storage units 175, the one or more machinelearning back-end units 180, the one or more analytics units 185, andthe one or more front-end units 190 can be implemented on a server local(e.g., on site at the manufacturer) to the one or more stations 105-115.In another example, the one or more machine learning back-end units 135,the one or more storage units 140 and analytics front-end units 145 canbe implemented on a cloud computing service remote from the one or morestations 105-115. In yet another example, the one or more data storageunits 175 and the one or more machine learning back-end units 180 can beimplemented remotely on a server of a vendor, and one or more datastorage units 175 and the one or more front-end units 190 areimplemented locally on a server or computer of the manufacturer. Inother examples, the one or more sensors 135-145, the one or more machinelearning back-end units 180, the one or more front-end unit 190, andother computing units of the action recognition and analytics system 100can perform processing at the edge of the network 192 in an edgecomputing implementation. The above example of the deployment of one ormore computing devices to implement the one or more interfaces 135-165,the one or more engines 170, the one or more data storage units 140 andone or more analytics front-end units 145, are just some of the manydifferent configuration for implementing the one or more machinelearning back-end units 135, one or more data storage units 140. Anynumber of computing devices, deployed locally, remotely, at the edge orthe like can be utilized for implementing the one or more machinelearning back-end units 135, the one or more data storage units 140, theone or more analytics front-end units 145 or other computing units.

The action recognition and analytics system 100 can also optionallyinclude one or more data compression units associated with one or moreof the interfaces 135-165. The data compression units can be configuredto compress or decompress data transmitted between the one or moreinterface 135-165, and the one or more engines 170. Data compression,for example, can advantageously allow the sensor data from the one ormore interface 135-165 to be transmitted across one or more existingnetworks 192 of a manufacturer. The data compression units can also beintegral to one or more interfaces 135-165 or implemented separately.For example, video capture sensors may include an integral MotionPicture Expert Group (MPEG) compression unit (e.g., H-264encoder/decoder). In an exemplary implementation, the one or more datacompression units can use differential coding and arithmetic encoding toobtain a 20× reduction in the size of depth data from depth sensors. Thedata from a video capture sensor can comprise roughly 30 GB of H.264compressed data per camera, per day for a factory operation with threeeight-hour shifts. The depth data can comprise roughly another 400 GB ofuncompressed data per sensor, per day. The depth data can be compressedby an algorithm to approximately 20 GB per sensor, per day. Together, aset of a video sensor and a depth sensor can generate approximately 50GB of compressed data per day. The compression can allow the actionrecognition and analytics system 100 to use a factory's network 192 tomove and store data locally or remotely (e.g., cloud storage).

The action recognition and analytics system 100 can also becommunicatively coupled to additional data sources 194, such as but notlimited to a Manufacturing Execution Systems (MES), warehouse managementsystem, or patient management system. The action recognition andanalytics system 100 can receive additional data, including one or moreadditional sensor streams, from the additional data sources 194. Theaction recognition and analytics system 100 can also output data, sensorstreams, analytics result and or the like to the additional data sources194. For example, the action recognition can identify a barcode on anobject and provide the barcode input to a MES for tracking.

The action recognition and analytics system 100 can continually measureaspects of the real-world, making it possible to describe a contextutilizing vastly more detailed data sets, and to solve importantbusiness problems like line balancing, ergonomics, and or the like. Thedata can also reflect variations over time. The one or more machinelearning back-end units 170 can be configured to recognize, in realtime, one or more cycles, processes, actions, sequences, objects,parameters and the like in the sensor streams received from theplurality of sensors 135-145. The one or more machine learning back-endunits 180 can recognize cycles, processes, actions, sequences, objects,parameters and the like in sensor streams utilizing deep learning,decision tree learning, inductive logic programming, clustering,reinforcement learning, Bayesian networks, and or the like.

Referring now to FIG. 2, an exemplary deep learning type machinelearning back-end unit, in accordance with aspects of the presenttechnology, is shown. The deep learning unit 200 can be configured torecognize, in real time, one or more cycles, processes, actions,sequences, objects, parameters and the like in the sensor streamsreceived from the plurality of sensors 120-130. The deep learning unit200 can include a dense optical flow computation unit 210, a ConvolutionNeural Networks (CNNs) 220, a Long Short Term Memory (LSTM) RecurrentNeural Network (RNN) 230, and a Finite State Automata (FSA) 240. TheCNNs 220 can be based on two-dimensional (2D) or three-dimensional (3D)convolutions. The dense optical flow computation unit 210 can beconfigured to receive a stream of frame-based sensor data 250 fromsensors 120-130. The dense optical flow computation unit 210 can beconfigured to estimate an optical flow, which is a two-dimension (2D)vector field where each vector is a displacement vector showing themovement of points from a first frame to a second frame. The CNNs 220can receive the stream of frame-based sensor data 250 and the opticalflow estimated by the dense optical flow computation unit 210. The CNNs220 can be applied to video frames to create a digest of the frames. Thedigest of the frames can also be referred to as the embedding vector.The digest retains those aspects of the frame that help in identifyingactions, such as the core visual clues that are common to instances ofthe action in question.

In a three-dimensional Convolution Neural Network (3D CNN) basedapproach, spatio-temporal convolutions can be performed to digestmultiple video frames together to recognize actions. For 3D CNN, thefirst two dimension can be along space, and in particular the width andheight of each video frame. The third dimension can be along time. Theneural network can learn to recognize actions not just from the spatialpattern in individual frame, but also jointly in space and time. Theneural network is not just using color patterns in one frame torecognize actions. Instead, the neural network is using how the patternshifts with time (i.e., motion cues) to come up with its classification.According the 3D CNN is attention driven, in that it proceeds byidentifying 3D spatio-temporal bounding boxes as Regions of Interest(RoI) and focusses on them to classify actions.

In one implementation, the input to the deep learning unit 200 caninclude multiple data streams. In one instance, a video sensor signal,which includes red, green and blue data streams, can comprise threechannels. Depth image data can comprise another channel. Additionalchannels can accrue from temperature, sound, vibration, data fromsensors (e.g., torque from a screwdriver) and the like. From the RGB anddepth streams, dense optical flow fields can be computed by the denseoptical flow computation unit 210 and fed to the Convolution NeuralNetworks (CNNs) 220. The RGB and depth streams can also be fed to theCNNs 220 as additional streams of derived data.

The Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) 230 canbe fed the digests from the output of the Convolution Neural Networks(CNNs) 220. The LSTM can essentially be a sequence identifier that istrained to recognize temporal sequences of sub-events that constitute anaction. The combination of the CNNs and LSTM can be jointly trained,with full back-propagation, to recognize low-level actions. Thelow-level actions can be referred to as atomic actions, like picking ascrew, picking a screwdriver, attaching screw to screwdriver and thelike. The Finite State Automata (FSA) 240 can be mathematical models ofcomputations that include a set of state and a set of rules that governthe transition between the states based on the provided input. The FSA240 can be configured to recognize higher-level actions 260 from theatomic actions. The high-level actions 260 can be referred to asmolecular actions, for example turning a screw to affix a hard drive toa computer chassis. The CNNs and LSTM can be configured to performsupervised training on the data from the multiple sensor streams. In oneimplementation, approximately 12 hours of data, collected over thecourse of several days, can be utilized to train the CNNs and LSTMcombination.

Referring now to FIG. 3, an exemplary Convolution Neural Networks (CNNs)and Long Short Term Memory (LSTM) Recurrent Neural Network (RNN), inaccordance with aspects of the present technology, is shown. The CNNscan include a frame feature extractor 310, a first Fully Connected (FC)layer 320, a Region of Interest (RoI) detector unit 330, a RoI poolingunit 340, and a second Fully Connected (FC) layer 350. The operation ofthe CNNs and LSTM will be further explained with reference to FIG. 4,which shows an exemplary method of detecting actions in a sensor stream.

The frame feature extractor 310 of the Convolution Neural Networks(CNNs) 220 can receive a stream of frame-based sensor data, at 410. At420, the frame feature extractor 310 can perform a two-dimensionalconvolution operation on the received video frame and generate atwo-dimensional array of feature vectors. The frame feature extractor310 can work on the full resolution image, wherein a deep network iseffectively sliding across the image generating a feature vector at eachstride position. Thus, each element of the 2D feature vector array is adescriptor for the corresponding receptive field (e.g., fixed portion ofthe underlying image). The first Fully Connected (FC) layer can flattenthe high-level features extracted by the frame feature extractor 310,and provide additional non-linearity and expressive power, enabling themachine to learn complex non-linear combinations of these features.

At 430, the RoI detector unit 330 can combine neighboring featurevectors to make a decision on whether the underlying receptive fieldbelongs to a Region of Interest (RoI) or not. If the underlyingreceptive field belongs to a RoI, a RoI rectangle can be predicted fromthe same set of neighboring feature vectors, at 440. At, 450, a RoIrectangle with a highest score can be chosen by the RoI detector unit330. For the chosen RoI rectangle, the feature vectors lying within itcan be aggregated by the RoI pooling unit 340, at 460. The aggregatedfeature vector is a digest/descriptor for the foreground for that videoframe.

In one implementation, the RoI detector unit 330 can determine a staticRoI. The static RoI identifies a Region of Interest (RoI) within anaggregate set of feature vectors describing a video frame, and generatesa RoI area for the identified RoI. A RoI area within a video frame canbe indicated with a RoI rectangle that encompasses an area of the videoframe designated for action recognition, such as an area in whichactions are performed in a process. Alternatively, the RoI area can bedesignated with a box, circle, highlighted screen, or any othergeometric shape or indicator having various scales and aspect ratiosused to encompass a RoI. The area within the RoI rectangle is the areawithin the video frame to be processed by the Long Short Term Memory(LSTM) for action recognition.

The Long Short Term Memory (LSTM) can be trained using a RoI rectanglethat provides, both, adequate spatial context within the video frame torecognize actions and independence from irrelevant portions of the videoframe in the background. The trade-off between spatial context andbackground independence ensures that the static RoI detector can provideclues for the action recognition while avoiding spurious unreliablesignals within a given video frame.

In another implementation, the RoI detector unit 330 can determine adynamic RoI. A RoI rectangle can encompass areas within a video frame inwhich an action is occurring. By focusing on areas in which actionoccurs, the dynamic RoI detector enables recognition of actions outsideof a static RoI rectangle while relying on a smaller spatial context, orlocal context, than that used to recognize actions in a static RoIrectangle.

In one implementation, the RoI pooling unit 340 extracts a fixed-sizedfeature vector from the area within an identified RoI rectangle, anddiscards the remaining feature vectors of the input video frame. Thefixed-sized feature vector, or foreground feature, includes the featurevectors generated by the video frame feature extractor that are locatedwithin the coordinates indicating a RoI rectangle as determined by theRoI detector unit 330. Because the RoI pooling unit 340 discards featurevectors not included within the RoI rectangle, the Convolution NeuralNetworks (CNNs) 220 analyzes actions within the RoI only, thus ensuringthat unexpected changes in the background of a video frame are noterroneously analyzed for action recognition.

In one implementation, the Convolution Neural Networks (CNNs) 220 can bean Inception ResNet. The Inception ResNet can utilize a sliding windowstyle operation. Successive convolution layers output a feature vectorat each point of a two-dimensional grid. The feature vector at location(x,y) at level 1 can be derived by weighted averaging features from asmall local neighborhood (aka receptive field) N around the (x,y) atlevel 1-1 followed by a pointwise non-linear operator. The non-linearoperator can be the RELU (max(0,x)) operator.

In the sliding window, there can be many more than 7×7 points at theoutput of the last convolution layer. A Fully Connected (FC) convolutioncan be taken over the feature vectors from the 7×7 neighborhoods, whichis nothing but applying one more convolution. The corresponding outputrepresents the Convolution Neural Networks (CNNs) output at the matching224×224 receptive field on the input image. This is fundamentallyequivalent to applying the CNNs to each sliding window stop. However, nocomputation is repeated, thus keeping the inferencing computation costreal time on Graphics Processing Unit (GPU) based machines.

The convolution layers can be shared between RoI detector 330 and thevideo frame feature extractor 310. The RoI detector unit 330 canidentify the class independent rectangular region of interest from thevideo frame. The video frame feature extractor can digest the videoframe into feature vectors. The sharing of the convolution layersimproves efficiency, wherein these expensive layers can be run once perframe and the results saved and reused.

One of the outputs of the Convolution Neural Networks (CNNs) is thestatic rectangular Region of Interest (RoI). The term “static” as usedherein denotes that the RoI does not vary greatly from frame to frame,except when a scene change occurs, and it is also independent of theoutput class.

A set of concentric anchor boxes can be employed at each sliding windowstop. In one implementation, there can be nine anchor boxes per slidingwindow stop for combinations of 3 scales and 3 aspect ratios. Therefore,at each sliding window stop there are two set of outputs. The first setof outputs can be a Region of Interest (RoI) present/absent thatincludes 18 outputs of the form 0 or 1. An output of 0 indicates theabsence of a RoI within the anchor box, and an output of 1 indicates thepresence of a RoI within the anchor box. The second set of outputs caninclude Bounding Box (BBox) coordinates including 36 floating pointoutputs indicating the actual BBox for each of the 9 anchor boxes. TheBBox coordinates are to be ignored if the RoI present/absent outputindicates the absence of a RoI.

For training, sets of video frames with a per-frame Region of Interest(RoI) rectangle are presented to the network. In frames without a RoIrectangle, a dummy 0×0 rectangle can be presented. The Ground Truth forindividual anchor boxes can be created via the Intersection over Union(IoU) of rectangles. For the ith anchor box

={xi, yi, wi, hi} the derived Ground Truth for the RoI presenceprobability can be determined by Equation 1:

$p_{1}^{*}\left\{ \begin{matrix}1 & {{{IoU}\left( {{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)}\mspace{14mu} \text{>=}\mspace{14mu} 0.7} \\0 & {{{IoU}\left( {{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)}\mspace{14mu} \text{<=}\mspace{14mu} 0.1} \\{{{box}\mspace{14mu} {unused}\mspace{14mu} {for}\mspace{14mu} {training}}\;} & \;\end{matrix} \right.$

where {right arrow over (g)}={x_(g), y_(g), w_(g), h_(g)}} is the GroundTruth RoI box for the entire frame.

The loss function can be determined by Equation 2:

${L\left( {p_{i},p_{i}^{*},{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)} = {\sum\limits_{i}{{- p_{i}^{*}}\log \; {p_{i}\left( {{S\left( {x_{i} - x_{s}} \right)} + {S\left( {y_{i} - y_{s}} \right)} + {S\left( {w_{i} - w_{s}} \right)} + {S\left( {h_{i} + h_{s}} \right)}} \right)}}}$

where pi is the predicted probability for presence of Region of Interest(RoI) in the ith anchor box and the smooth loss function can be definedby Equation 3:

${S(x)} = \left\{ \begin{matrix}{0.5x^{2}} & {{x} < 1} \\{{x} - 0.5} & {otherwise}\end{matrix} \right.$

The left term in the loss function is the error in predicting theprobability of the presence of a RoI, while the second term is themismatch in the predicted Bounding Box (BBox). It should be noted thatthe second term vanishes when the ground truth indicates that there isno RoI in the anchor box.

The static Region of Interest (RoI) is independent of the action class.In another implementation, a dynamic Region of Interest (RoI), that isclass dependent, is proposed by the CNNs. This takes the form of arectangle enclosing the part of the image where the specific action isoccurring. This increases the focus of the network and takes it a stepcloser to a local context-based action recognition.

Once a Region of Interest (RoI) has been identified, the frame featurecan be extracted from within the RoI. These will yield a backgroundindependent frame digest. But this feature vector also needs to be afixed size so that it can be fed into the Long Short Term Memory (LSTM).The fixed size can be achieved via RoI pooling. For RoI pooling, the RoIcan be tiled up into 7×7 boxes. The mean of all feature vectors within atile can then be determined. Thus, 49 feature vectors that areconcatenated from the frame digest can be produced. The second FullyConnected (FC) layer 350 can provide additional non-linearity andexpressive power to the machine, creating a fixed size frame digest thatcan be consumed by the LSTM 230.

At 470, successive foreground features can be fed into the Long ShortTerm Memory (LSTM) 230 to learn the temporal pattern. The LSTM 230 canbe configured to recognize patterns in an input sequence. In videoaction recognition, there could be patterns within sequences of framesbelonging to a single action, referred to as intra action patterns.There could also be patterns within sequences of actions, referred to asinter action patterns. The LSTM can be configured to learn both of thesepatterns, jointly referred to as temporal patterns. The Long Short TermMemory (LSTM) analyzes a series of foreground features to recognizeactions belonging to an overall sequence. In one implementation, theLSTM outputs an action class describing a recognized action associatedwith an overall process for each input it receives. In anotherimplementation, each class action is comprised of sets of actionsdescribing actions associated with completing an overall process. Eachaction within the set of actions can be assigned a score indicating alikelihood that the action matches the action captured in the inputvideo frame. Each action may be assigned a score such that the actionwith the highest score is designated the recognized action class.

Foreground features from successive frames can be feed into the LongShort Term Memory (LSTM). The foreground feature refers to theaggregated feature vectors from within the Region of Interest (RoI)rectangles. The output of the LSTM at each time step is the recognizedaction class. The loss for each individual frame is the cross entropysoftmax loss over the set of possible action classes. A batch is definedas a set of three randomly selected set of twelve frame sequences in thevideo stream. The loss for a batch is defined as the frame loss averagedover the frame in the batch. The numbers twelve and three are choseempirically. The overall LSTM loss function is given by Equation 4:

${L\left( {B_{i}\left\{ {S_{1},S_{2},{.\;.\;.}\mspace{14mu},S_{B}} \right\}} \right)} = {\sum\limits_{k = 1}^{B}{\sum\limits_{i = 1}^{S_{k}}{\sum\limits_{l = 1}^{A}{{- \left( \frac{e^{a_{t_{i}}}}{\sum\limits_{l = 1}^{A}e^{a_{\;^{t_{ji}}}}} \right)}\log \mspace{11mu} a_{t_{i}}^{*}}}}}$

where B denotes a batch of ∥B∥ frame sequences {S₁, S₂, . . . ,S_(∥B∥)}. S_(k) comprises a sequence of ∥S_(k)∥ frames, wherein in thepresent implementation ∥B∥=3 and ∥S_(k)∥=12k. A denotes the set of allaction classes, a_(tl) denotes the ith action class score for the tthframe from LSTM and a_(tl) ^(o) denotes the corresponding Ground Truth.

Referring again to FIG. 1, the machine learning back-end unit 135 canutilize custom labelling tools with interfaces optimized for labelingRoI, cycles and action. The labelling tools can include both standaloneapplication built on top of Open source Computer Vision (OpenCV) and webbrowser application that allow for the labeling of video segment.

Referring now to FIG. 5, an action recognition and analytics system, inaccordance with aspect of the present technology, is shown. Again, theaction recognition and analytics system 500 can be deployed in amanufacturing, health care, warehousing, shipping, retail, restaurant,or similar context. The system 500 similarly includes one or moresensors 505-515 disposed at one or more stations, one or more machinelearning back-end units 520, one or more analytics units 525, and one ormore front-end units 530. The one or more sensors 505-515 can be coupledto one or more local computing devices 535 configured to aggregate thesensor data streams from the one or more sensors 505-515 fortransmission across one or more communication links to a streaming mediaserver 540. The streaming media server 540 can be configured to receivedone or more streams of sensor data from the one or more sensors 505-515.A format converter 545 can be coupled to the streaming media server 540to receive the one or more sensor data streams and convert the sensordata from one format to another. For example, the one or more sensorsmay generate Motion Picture Expert Group (MPEG) formatted (e.g., H.264)video sensor data, and the format converter 545 can be configured toextract frames of JPEG sensor data. An initial stream processor 550 canbe coupled to the format convert 555. The initial stream processor 550can be configured to segment the sensor data into pre-determined chucks,subdivide the chunks into key frame aligned segment, and create persegment sensor data in one or more formats. For example, the initialstream processor 550 can divide the sensor data into five minute chunks,subdivide the chunks into key frame aligned segment, and convert the keyframe aligned segments into MPEG, MPEG Dynamic Adaptive Streaming overHypertext Transfer Protocol (DASH) format, and or the like. The initialstream processor 550 can be configured to store the sensor streamsegments in one or more data structures for storing sensor streams 555.In one implementation, as sensor stream segments are received, each newsegment can be appended to the previous sensor stream segments stored inthe one or more data structures for storing sensor streams 555.

A stream queue 560 can also be coupled to the format converter 545. Thestream queue 560 can be configured to buffer the sensor data from theformat converter 545 for processing by the one or more machine learningback-end units 520. The one or more machine learning back-end units 520can be configured to recognize, in real time, one or more cycles,processes, actions, sequences, objects, parameters and the like in thesensor streams received from the plurality of sensors 505-515. Referringnow to FIG. 6, an exemplary method of detecting actions, in accordancewith aspects of the present technology, is shown. The action recognitionmethod can include receiving one or more sensor streams from one or moresensors, at 610. In one implementation, one or more machine learningback-end units 520 can be configured to receive sensor streams fromsensors 505-515 disposed at one or more stations.

At 620, a plurality of processes including one or more actions arrangedin one or more sequences and performed on one or more objects, and oneor more parameters can be detected. in the one or more sensor streams.At 630, one or more cycles of the plurality of processes in the sensorstream can also be determined. In one implementation, the one or moremachine learning back-end units 520 can recognize cycles, processes,actions, sequences, objects, parameters and the like in sensor streamsutilizing deep learning, decision tree learning, inductive logicprogramming, clustering, reinforcement learning, Bayesian networks, andor the like.

At 640, indicators of the one or more cycles, one or more processes, oneor more actions, one or more sequences, one or more objects, and one ormore parameters can be generated. In one implementation, the one or moremachine learning back-end units 520 can be configured to generateindicators of the one or more cycles, processes, actions, sequences,objects, parameters and or the like. The indicators can includedescriptions, identifiers, values and or the like associated with thecycles, processes, actions, sequences, objects, and or parameters. Theparameters can include, but is not limited to, time, duration, location(e.g., x, y, z, t), reach point, motion path, grid point, quantity,sensor identifier, station identifier, and bar codes.

At 650, the indicators of the one or more cycles, one or more processes,one or more actions, one or more sequences, one or more objects, and oneor more parameters indexed to corresponding portions of the sensorstreams can be stored in one or more data structures for storing datasets 565. In one implementation, the one or more machine learningback-end units 520 can be configured to store a data set including theindicators of the one or more processes, one or more actions, one ormore sequences, one or more objects, and one or more parameters for eachcycle. The data sets can be stored in one or more data structures forstoring the data sets 565. The indicators of the one or more cycles, oneor more processes, one or more actions, one or more sequences, one ormore objects, and one or more parameters in the data sets can be indexedto corresponding portion of the sensor streams in one or more datastructures for storing sensor streams 555.

In one implementation, the one or more streams of sensor data and theindicators of the one or more of the plurality of cycles, one or moreprocesses, one or more actions, one or more sequences, one or moreobjects and one or more parameters indexed to corresponding portion ofthe one or more streams of sensor data can be encrypted when stored toprotect the integrity of the streams of sensor data and or the datasets. In one implementation, the one or more streams of sensor data andthe indicators of the one or more of the plurality of cycles, one ormore processes, one or more actions, one or more sequences, one or moreobjects and one or more parameters indexed to corresponding portion ofthe one or more streams of sensor data can be stored utilizing blockchaining. The blockchaining can be applied across the cycles, sensorstreams, stations, supply chain and or the like. The blockchaining caninclude calculating a cryptographic hash based on blocks of the datasets and or blocks of the streams of sensor data. The data sets, streamsof sensor data and the cryptographic hash can be stored in one or moredata structures in a distributed network.

Referring again to FIG. 5, the one or more analytics units 525 can becoupled to the one or more data structures for storing the sensorstreams 555, one or more data structures for storing the data set 565,one or more additional sources of data 570, one or more data structuresfor storing analytics 575. The one or more analytics units 525 can beconfigured to perform statistical analysis on the cycle, process,action, sequence, object and parameter data in one or more data sets.The one or more analytics units 525 can also utilize additional datareceived from one or more additional data sources 570. The additionaldata sources 570 can include, but are not limited to, ManufacturingExecution Systems (MES), warehouse management system, or patientmanagement system, accounting systems, robot datasheets, human resourcerecords, bill of materials, and sales systems. Some examples of datathat can be received from the additional data sources 570 can include,but is not limited to, time, date, shift, day of week, plant, factory,assembly line, sub-assembly line, building, room, supplier, work space,action capability, and energy consumption, ownership cost. The one ormore analytics units 525 can be configured to utilize the additionaldata from one or more additional source of data 570 to update, correct,extend, augment or the like, the data about the cycles, processes,action, sequences, objects and parameters in the data sets. Similarly,the additional data can also be utilized to update, correct, extend,augment or the like, the analytics generate by the one or more analyticsfront-end units 525. The one or more analytics units 525 can also storetrends and other comparative analytics utilizing the data sets and orthe additional data, can use sensor fusion to merge data from multiplesensors, and other similar processing and store the results in the oneor more data structures for storing analytics 575. In oneimplementation, one or more engines 170, such as the one or more machinelearning back-end units 520 and or the one or more analytics units 525,can create a data structure including a plurality of data sets, the datasets including one or more indicators of at least one of one or morecycles, one or more processes, one or more actions, one or moresequences, one or more object and one or more parameters. The one ormore engine 170 can build the data structure based on the one of one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more object and one or more parameters detected in theone or more sensor streams. The data structure definition, configurationand population can be performed in real time based upon the content ofthe one or more sensor streams. For example, Table 1 shows a tabledefined, configured and populated as the sensor streams are processed bythe one or more machine learning back-end unit 520.

TABLE 1 ENTITY ID DATA STUCTURE (TABLE 1) MOTHER- FRAME HUMAN HAND ARMLEG BOARD SCREW 1 Yes Yes Yes Yes YES Yes 2 Yes No No Yes Yes No 3 YesYes Yes Yes YES Yes

The data structure creation process can continue to expand upon theinitial structure and or create additional data structures base uponadditional processing of the one or more sensor streams.

In one embodiment, the status associated with entities is added to adata structure configuration (e.g., engaged in an action, subject to aforce, etc.) based upon processing of the access information. In oneembodiment, activity associated with the entities is added to a datastructure configuration (e.g., engaged in an action, subject to a force,etc.) based upon processing of the access information. One example ofentity status data set created from processing of above entity ID dataset (e.g., motion vector analysis of image object, etc.) is illustratedin Table 2.

TABLE 2 ENTITY STATUS DATA STRUCTURE (TABLE 2) HAND ARM LEG HUMAN FRAMEMOVING MOVING MOVING MOVING 1 Yes Yes No Yes 2 No No Yes No 3 Yes YesYes Yes

In one embodiment, a third-party data structure as illustrated in Table3 can be accessed.

TABLE 3 OSHA DATA STRUCTURE (TABLE 3) SAFE TO SAFE TO ACTIVITY MOVE LEGMOVE HAND SCREWING TO No Yes MOTHERBOARD LIFTING Yes Yes HOUSING

In one embodiment, activity associated with entities is added to a datastructure configuration (e.g., engaged in an action, subject to a force,etc.) based upon processing of the access information as illustrated inTable 4.

TABLE 4 ACTIVITY DATA STRUCTURE (TABLE 4) SCREWING TO HUMAN MOTHERBOARDFRAME MOTHERBOARD ACTION SAFE COMPLETE 1 Yes Yes Yes 2 No NA NO 3 Yes NOYes

Table 4 is created by one or more engines 170 based on furtheranalytics/processing of info in Table 1, Table 2 and Table 3. In oneexample, Table 4 is automatically configured to have a column forscrewing to motherboard. In frames 1 and 3 since hand is moving (seeTable 2) and screw present (see Table 1), then screwing to motherboard(see Table 3). In frame 2, since hand is not moving (see Table 2) andscrew not present (see Table 1), then no screwing to motherboard (seeTable 3).

Table 4 is also automatically configured to have a column for humanaction safe. In frame 1 since leg not moving in frame (see Table 2) theworker is safely (see Table 3) standing at workstation while engage inactivity of screwing to motherboard. In frame 3 since leg moving (seeTable 2) the worker is not safely (see Table 3) standing at workstationwhile engage in activity of screwing to motherboard.

The one or more analytics units 525 can also be coupled to one or morefront-end units 580. The one or more front-end units 575 can include amentor portal 580, a management portal 585, and other similar portals.The mentor portal 550 can be configured for presenting feedbackgenerated by the one or more analytics units 525 and or the one or morefront-end units 575 to one or more actors. For example, the mentorportal 580 can include a touch screen display for indicatingdiscrepancies in the processes, actions, sequences, objects andparameters at a corresponding station. The mentor portal 580 could alsopresent training content generated by the one or more analytics units525 and or the one or more front-end units 575 to an actor at acorresponding station. The management port 585 can be configured toenable searching of the one or more data structures storing analytics,data sets and sensor streams. The management port 585 can also beutilized to control operation of the one or more analytics units 525 forsuch functions as generating training content, creating work charts,performing line balancing analysis, assessing ergonomics, creating jobassignments, performing causal analysis, automation analysis, presentingaggregated statistics, and the like.

The action recognition and analytics system 500 can non-intrusivelydigitize processes, actions, sequences, objects, parameters and the likeperformed by numerous entities, including both humans and machines,using machine learning. The action recognition and analytics system 500enables human activity to be measured automatically, continuously and atscale. By digitizing the performed processes, actions, sequences,objects, parameters, and the like, the action recognition and analyticssystem 500 can optimize manual and/or automatic processes. In oneinstance, the action recognition and analytics system 500 enables thecreation of a fundamentally new data set of human activity. In anotherinstance, the action recognition and analytics system 500 enables thecreation of a second fundamentally new data set of man and machinecollaborating in activities. The data set from the action recognitionand analytics system 500 includes quantitative data, such as whichactions were performed by which person, at which station, on whichspecific part, at what time. The data set can also include judgementsbased on performance data, such as does a given person perform better orworse that average. The data set can also include inferences based on anunderstanding of the process, such as did a given product exited theassembly line with one or more incomplete tasks.

Referring now to FIG. 7, an action recognition and analytics system, inaccordance with aspects of the present technology, is shown. The actionrecognition and analytics system can include a plurality of sensorlayers 702, a first Application Programming Interface (API) 704, aphysics layer 706, a second API 708, a plurality of data 710, a thirdAPI 712, a plurality of insights 714, a fourth API 716 and a pluralityof engine layers 718. The sensor layer 702 can include, for example,cameras at one or more stations 720, MES stations 722, sensors 724, IIoTintegrations 726, process ingestion 728, labeling 730, neural networktraining 732 and or the like. The physics layer 706 captures data fromthe sensor layer 702 to passes it to the data layer 710. The data layer710, can include but not limited to, video and other streams 734, +NNannotations 736, +MES 738, +OSHA database 740, and third-party data 742.The insights layer 714 can provide for video search 744, time seriesdata 746, standardized work 748, and spatio-temporal 842. The enginelayer 718 can be utilized for inspection 752, lean/line balancing 754,training 756, job assignment 758, other applications 760, quality 763,traceability 764, ergonomics 766, and third party applications 768.

Referring now to FIG. 8, an exemplary station, in accordance withaspects of the present technology, is shown. The station 800 is an areasassociated with one or more cycles, processes, actions, sequences,objects, parameters and or the like, herein also referred to asactivity. Information regarding a station can be gathered and analyzedautomatically. The information can also be gathered and analyzed in realtime. In one exemplary implementation, an engine participates in theinformation gathering and analysis. The engine can use ArtificialIntelligence to facilitate the information gathering and analysis. It isappreciated there can be many different types of stations with variousassociated entities and activities. Additional descriptions of stations,entities, activities, information gathering, and analytics are discussedin other sections of this detailed description.

A station or area associated with an activity can include variousentities, some of which participate in the activity within the area. Anentity can be considered an actor, an object, and so on. An actor canperform various actions on an object associated with an activity in thestation. It is appreciated a station can be compatible with varioustypes of actors (e.g., human, robot, machine, etc.). An object can be atarget object that is the target of the action (e.g., thing being actedon, a product, a tool, etc.). It is appreciated that an object can be atarget object that is the target of the action and there can be varioustypes of target objects (e.g., component of a product or article ofmanufacture, an agricultural item, part of a thing or person beingoperated on, etc.). An object can be a supporting object that supports(e.g., assists, facilitates, aids, etc.) the activity. There can bevarious types of supporting objects, including load bearing components(e.g., a work bench, conveyor belt, assembly line, table top etc.), atool (e.g., drill, screwdriver, lathe, press, etc.), a device thatregulates environmental conditions (e.g., heating ventilating and airconditioning component, lighting component, fire control system, etc.),and so on. It is appreciated there can be many different types ofstations with a various entities involved with a variety of activities.Additional descriptions of the station, entities, and activities arediscussed in other sections of this detailed description.

The station 800 can include a human actor 810, supporting object 820,and target objects 830 and 840. In one embodiment, the human actor 810is assembling a product that includes target objects 830, 840 whilesupporting object 820 is facilitating the activity. In one embodiment,target objects 830, 840 are portions of a manufactured product (e.g., amotherboard and a housing of an electronic component, a frame and amotor of a device, a first and a second structural member of anapparatus, legs and seat portion of a chair, etc.). In one embodiment,target objects 830, 840 are items being loaded in a transportationvehicle. In one embodiment, target objects 830, 840 are products beingstocked in a retail establishment. Supporting object 820 is a loadbearing component (e.g., a work bench, a table, etc.) that holds targetobject 840 (e.g., during the activity, after the activity, etc.). Sensor850 senses information about the station (e.g., actors, objects,activities, actions, etc.) and forwards the information to one or moreengines 860. Sensor 850 can be similar to sensor 135. Engine 860 caninclude a machine learning back end component, analytics, and front endsimilar to machine learning back end unit 180, analytics unit 190, andfront end 190. Engine 860 performs analytics on the information and canforward feedback to feedback component 870 (e.g., a display, speaker,etc.) that conveys the feedback to human actor 810.

Referring now to FIG. 9, an exemplary station, in accordance withaspects of the present technology, is shown. The station 900 includes arobot actor 910, target objects 920, 930, and supporting objects 940,950. In one embodiment, the robot actor 910 is assembling target objects920, 930 and supporting objects 940, 950 are facilitating the activity.In one embodiment, target objects 920, 930 are portions of amanufactured product. Supporting object 940 (e.g., an assembly line, aconveyor belt, etc.) holds target objects 920, 930 during the activityand moves the combined target object 920, 930 to a subsequent station(not shown) after the activity. Supporting object 940 provides areasupport (e.g., lighting, fan temperature control, etc.). Sensor 960senses information about the station (e.g., actors, objects, activities,actions, etc.) and forwards the information to engine 970. Engine 970performs analytics on the information and forwards feedback to acontroller 980 that controls robot 910. Engine 970 can be similar toengine 170 and sensor 960 can be similar to sensor 135.

A station can be associated with various environments. The station canbe related to an economic sector. A first economic sector can includethe retrieval and production of raw materials (e.g., raw food, fuel,minerals, etc.). A second economic sector can include the transformationof raw or intermediate materials into goods (e.g., manufacturingproducts, manufacturing steel into cars, manufacturing textiles intoclothing, etc.). A third sector can include the supply and delivery ofservices and products (e.g., an intangible aspect in its own right,intangible aspect as a significant element of a tangible product, etc.)to various parties (e.g., consumers, businesses, governments, etc.). Inone embodiment, the third sector can include sub sectors. One sub sectorcan include information and knowledge-based services. Another sub sectorcan include hospitality and human services. A station can be associatedwith a segment of an economy (e.g., manufacturing, retail, warehousing,agriculture, industrial, transportation, utility, financial, energy,healthcare, technology, etc,). It is appreciated there can be manydifferent types of stations and corresponding entities and activities.Additional descriptions of the station, entities, and activities arediscussed in other sections of this detailed description.

In one embodiment, station information is gathered and analyzed. In oneexemplary implementation, an engine (e.g., an information processingengine, a system control engine, an Artificial Intelligence engine,etc.) can access information regarding the station (e.g., information onthe entities, the activity, the action, etc.) and utilizes theinformation to perform various analytics associated with the station. Inone embodiment, engine can include a machine learning back end unit,analytics unit, front end unit, and data storage unit similar to machinelearning back end 180, analytics 185, front end 190 and data storage175. In one embodiment, a station activity analysis process isperformed. Referring now to FIG. 10, an exemplary station activityanalysis method, in accordance with one embodiment, is shown.

At 1010, information regarding the station is accessed. In oneembodiment, the information is accessed by an engine. The informationcan be accessed in real time. The information can be accessed frommonitors/sensors associated with a station. The information can beaccessed from an information storage repository. The information caninclude various types of information (e.g., video, thermal. optical,etc.). Additional descriptions of the accessing information arediscussed in other sections of this detailed description

At 1020, information is correlated with entities in the station andoptionally with additional data sources. In one embodiment, theinformation the correlation is established at least in part by anengine. The engine can associate the accessed information with an entityin a station. An entity can include an actor, an object, and so on.Additional descriptions of the correlating information with entities arediscussed in other sections of this detailed description.

At 1030, various analytics are performed utilizing the accessedinformation at 1010, and correlations at 1020. In one embodiment, anengine utilizes the information to perform various analytics associatedwith station. The analytics can be directed at various aspects of anactivity (e.g., validation of actions, abnormality detection, training,assignment of actor to an action, tracking activity on an object,determining replacement actor, examining actions of actors with respectto an integrated activity, automatic creation of work charts, creatingergonomic data, identify product knitting components, etc.) Additionaldescriptions of the analytics are discussed in other sections of thisdetailed description.

At 1040, optionally, results of the analysis can be forwarded asfeedback. The feedback can include directions to entities in thestation. In one embodiment, the information accessing, analysis, andfeedback are performed in real time. Additional descriptions of thestation, engine, entities, activities, analytics and feedback arediscussed in other sections of this detailed description,

It is also appreciated that accessed information can include generalinformation regarding the station (e.g., environmental information,generic identification of the station, activities expected in station, agolden rule for the station, etc.). Environmental information caninclude ambient aspects and characteristics of the station (e.g.,temperature, lighting conditions, visibility, moisture, humidity,ambient aroma, wind, etc.).

It also appreciated that some of types of characteristics or featurescan apply to a particular portion of a station and also the generalenvironment of a station. In one exemplary implementation, a portion ofa station (e.g., work bench, floor area, etc.) can have a firstparticular visibility level and the ambient environment of the stationcan have a second particular visibility level. It is appreciated thatsome of types of characteristics or features can apply to a particularentity in a station and also the station environment. In one embodiment,an entity (e.g., a human, robot, target object, etc.) can have a firstparticular temperature range and the station environment can have asecond particular temperature range.

The action recognition and analytics system 100, 500 can be utilized forprocess validation, anomaly detection and/or process quality assurancein real time. The action recognition and analytics system 100, 500 canalso be utilized for real time contextual training. The actionrecognition and analytics system 100, 500 can be configured forassembling training libraries from video clips of processes to speed newproduct introductions or onboard new employees. The action recognitionand analytics system 100, 500 can also be utilized for line balancing byidentifying processes, sequences and/or actions to move among stationsand implementing lean processes automatically. The action recognitionand analytics system 100, 500 can also automatically create standardizedwork charts by statistical analysis of processes, sequences and actions.The action recognition and analytics system 100, 500 can alsoautomatically create birth certificate videos for a specific unit. Theaction recognition and analytics system 100, 500 can also be utilizedfor automatically creating statistically accurate ergonomics data. Theaction recognition and analytics system 100, 500 can also be utilized tocreate programmatic job assignments based on skills, tasks, ergonomicsand time. The action recognition and analytics system 100, 500 can alsobe utilized for automatically establishing traceability including forcausal analysis. The action recognition and analytics system 100, 500can also be utilized for kitting products, including real timeverification of packing or unpacking by action and image recognition.The action recognition and analytics system 100, 500 can also beutilized to determine the best robot to replace a worker when ergonomicproblems are identified. The action recognition and analytics system100, 500 can also be utilized to design an integrated line of humans andcobot and/or robots. The action recognition and analytics system 100,500 can also be utilized for automatically programming robots based onobserving non-modeled objects in the work space.

The deep learning systems and methods can be directed to actualconditions/objects that exist. Based on the statistical properties ofthe data collected by the deep learning systems or the knowledge of theprocess engineer, a golden process can be identified. In one embodiment,a golden process is an optimal solution (in terms of locations, actions,objects, conditions, etc,) to maximize productivity subject to cost,labor, machine and other constraints. The conditions/objects may beintroduced to the activity space but not be ones included in theprevious/golden process. In one embodiment, activity of actors (e.g.,human, robot, machine, etc.) is monitored and analyzed, includingdetermining if the condition/object could potentially interferes withthe actor. Programing robots on the plant floor is often atime-consuming process, especially if a large number of stations have tobe prepared for production. Simulation tools like those from DassaultSystems and Siemens are commonly used with CAD data to simulate themotion of the chosen robots and optimal paths are planned a priori. Oncethe plant is available for re-tooling, the robot programmer downloadsthe simulated path (with 3D position and orientation as well as velocityinformation). However, objects (e.g. cables, hose, additional toolingetc.) not present in the simulation models cause the robot arm to runinto obstacles. In a conventional approach a robot programmer is oftenleft having to completely reprogram the robot's motion plan, losing thebenefits of pre-programming. Most robot simulators lack the ability toview and analyze the workstation and are unable to help withreprogramming the originally simulated path.

The engine systems and methods can observe actors (e.g., robots, otherprogrammable machines, humans, cobots, etc.), and the physicalenvironment they are in, to detect “foreign” objects in the scene, andto flag potential collision points. In one embodiment, a deep learningsystem and method constructs a “picture” of the workspace as itcurrently exist. The deep learning system and method can, either on itsown or in partnership with robot simulation products, identify collisionpoints in the robot's path. Representing these newly observed solidobjects, it can help update the geometric models, leaving the simulationpackage to discover newer, collision-free paths that the robotprogrammer can start working with. The deep learning system and methodsaves effort, as originally desired.

It is appreciated that various combinations of actors can be monitoredand analyzed. For example, a human and a robot, a robot and a robot, ahuman and a plurality of other humans, a human and a plurality ofrobots, a robot and a plurality of other robots, and so on.

Conventionally, robots are frequently installed with protective fencesto ensure that workers and robots do not work in overlapping spaces.Work in the 1990's by Akella et al lead to the development of cobots(“collaborative robots”) that were designed to safely work with humanworkers. The cobot market, projected to be a $12B industry by 2025[Robotics Industries Association], is increasingly driving the need toensure that people and cobots (and even robots) can be positioned towork together safely. The most common conventional solution in theabsence of accurate and complete worker motion data is to beconservative in the selection of the cobots/robots or in their locationrelative to the nearest humans.

In one embodiment, a deep learning system can observe cobots/robotsexecuting tasks. The engine observes the cobots/robots just as easily asit can observe humans performing their tasks. In one exemplaryimplementation observing robots is much easier, since the variability intheir motion is much lower.

Having observed both humans and robots working, the deep learning systemcan provide recommendations to optimally ensure safe and productiveways. For human data, if there are ergonomic hazards, the time andmotion study can be used to identify an appropriate robot from a libraryof robots available. For robot data, identify tasks that might be betterserved for humans because of challenges that the robot system might befacing. For humans and robots data, a deep learning system can helpminimize the footprint based on motions of robots and humans workingtogether. In other words, run the same design process on a human-machinesystem.

A deep learning system can observe the actors (e.g., human and machine,cobot and robot, etc.) at work. The deep learning system determines thework envelopes and time and motion studies of both actors, substitutingrepresentative and probabilistic models for them when required forsimulation purposes. In one exemplary implementation, safety is theprimary cost function, it identifies co-working spaces by overlayingreach, motion and action data. The deep learning system can usemathematical techniques from simply overlaying spatial work envelopes tospatio-temporal representations to determine the relative positions ofthe assembly line, the worker and the robot/cobot system, theirkinematic and dynamic properties as well as safer parts of their workenvelopes.

FIG. 11 is a block diagram of activity space 1100 with an actor inaccordance with one embodiment. Activity space 1100 includes actor 1110with access to reach points 1121, 1124 a, 1124 b, 1125 a, 1125 b, and1127. The actor 1110 is shown coupling two product components by movinga first product component at reach point 1124 a to reach point 1124 bvia motion 1131 and a second component at reach point 1125 a to reachpoint 1125 b via motion 1132.

FIG. 12 is a block diagram of activity space 1200 with an actor inaccordance with one embodiment. Activity space 1200 includes actor 1210with access to reach points 1221, 1224 a, 1224 b, 1225 a, 1225 b, and1227. The actor 1210 is shown failing to coupling two product componentsby previously unmodeled object 1222). The actor can move a first productcomponent at reach point 1224 a to reach point 1124 b via motion 1131but can not move a second component at reach point 1125 a via a similarmotion.

In one embodiment, an engine creates a model for an activity space.

FIG. 13 is a flow chart of an automated actor modeling method 1300 inaccordance with one embodiment.

In block 1310, information associated with a first actor is accessed,including sensed activity information associated with an activity space.The activity space can include a task space associated with performanceof a task. In one embodiment, the sensed activity information isassociated with a grid within the activity space. The first actor can bea human and the second actor is a device. The first actor is a human andthe second actor is a human. The first actor is a device and the secondactor is a device.

In block 1320, the activity information is analyzed, including analyzingactivity of the first actor with respect to a plurality of other actors.In one embodiment, the analyzing includes: comparing informationassociated with activity of the first actor within the activity spacewith anticipated activity of the respective ones of the plurality of theactors within the activity space; and analyzing/comparing deviationsbetween the activity of the first actor and the anticipated activity ofthe second actor.

In one exemplary implementation, the analyzing comprises: determining ifthe deviation associated with a respective one of the plurality ofactors is within an acceptable threshold (e.g., limit/parameter,measurement, accuracy, payload, etc.), and identifying the respectiveone of the plurality of other actors as a potential acceptable candidateto be the replacement actor when the deviation associated with arespective one of the plurality of actors is within the acceptablethreshold. The analyzing can further comprise eliminating the respectiveone of the plurality of other actors as a potential acceptable candidateto be the replacement actor when the deviation associated with arespective one of the plurality of actors is not within the acceptablethreshold. The analyzing can include automated artificial intelligenceanalysis.

In block 1330, feedback on the results of the analysis is forwarded. Inone embodiment, the results include identification of a second actor asa replacement actor to replace the first actor, wherein the second actoris one of the plurality of other actors.

Referring now to FIG. 14, a side view of an exemplary reach scenario ina station is shown in accordance with one embodiment. The reach scenarioshows six reach locations A through F. The locations can correspond to azone (e.g., a primary reach zone, a secondary reach zone, a tertiaryreach zone, etc.). The reach locations can be associated with activities(e.g., motions, tasks, actions, etc.) performed by actor 1410.

In one embodiment, an engine performs an analysis of the reach scenarioand corresponding activities. The analysis can include identification ofthe reach points (e.g., based on sensed/monitored information from thestation, third party information, etc.). In one embodiment, the enginegenerates a data structure based on the analysis of an activity in astation. The data structure can include information associated with areach scenario. The table below is a block diagram of an exemplary datastructure in accordance with one embodiment of the present invention. Inone exemplary implementation, the table corresponds to the reachscenario in FIG. 14.

TABLE 5 Location A B C D E F Reach 455 955 357 1165 357 245 Distance(Millimeter) Frequency 321 311 311 331 421 321 Payload 2 2 3 2 2.5 4Weight (Grams) Torque 8.92 18.73 10.50 22.85 8.75 9.61 (NortonMillimeters)

The location row includes identification of the locations of the reachpoint. The reach distance row includes distances of the reach point fromthe actor. The frequency is the number of time the actor motions to thereach point location. The payload weight is the weight of an object theactor is supporting/holding at the reach point. It is appreciated thatpayload weights can change as components are added/removed due to anactivity at a reach point. In one embodiment, the heaviest weight of theobject (e.g., before component is removed, after a component is added,etc.) at a reach point is used to calculate torque at a reach point. Anengine can access a sensed measurement of a torque, a calculated torque(e.g., based on the distance of an object from a moment/twist point andweight of an object, etc.).

Referring now to FIG. 15 is a top down view of another exemplary reachscenario in a station is shown in accordance with one embodiment. Thereach scenario shows six reach locations U through Z. The locations cancorrespond to a zone (e.g., a primary reach zone, a secondary reachzone, a tertiary reach zone, etc.). The reach locations can beassociated with activities (e.g., motions, tasks, actions, etc.)performed by actor 1510.

In one embodiment, an engine performs an analysis of the reach scenarioand corresponding activities. The analysis can include identification ofthe reach points (e.g., based on sensed/monitored information from thestation, third party information, etc.). The engine analysis can alsoinclude identification of zones (e.g., a primary zone, a secondary,zone, a tertiary zone, etc.) in which a respective one or more of thereach points are located. The zones can correspond to differentcharacteristics (e.g., distance from an actor, load constraints, etc.).In one embodiment, a primary zone and tertiary zone (e.g., respectivelyclose and far distances from the actor) correspond to locations that arerelatively awkward to reach (e.g., need to lean back, need to leanforward, pick up parts from a particular orientation, etc.). In oneexemplary implementation, a load limit in a secondary zone can be higherthan a tertiary zone (e.g., the tertiary zone can put higher torquestrain on an actor's supporting member/component, arm, joint, pivotpoint, etc.).

In one embodiment, the engine generates a data structure based on theanalysis of an activity in a station. The data structure can includeinformation attached kinematics (e.g., attributes, motion, reach,velocity, etc.) and matched dynamics (e.g., torque, force, etc.)associated with a reach scenario. In one embodiment, the data structurecan include spatio-temporal information related to activities of anactor in the work space. FIG. 16 is a block diagram of an exemplary datastructure in accordance with one embodiment. The activity andcorresponding starting coordinates and ending coordinates can be Anengine can produce motion data from worker's hands starting position(e.g., x, y, z, roll, pitch and yaw) to ending position (e.g., x1, y1,z1, roll1, pitch1, yaw1). The motion data can include the movementdistance P(x,y,z) minus P(x1, y1, z1). It can also be computed in sixdimensional space. This data can be analyzed over a time-period forrepetitive motions to identify ergonomic issues developing across longperiods. This is shown by the dates and times, identified actions,starting coordinates, and in coordinates, the weight of the item, andthe movement distance. In one embodiment, a correlation to between theactivity and a reach point is established. The activities can be similarto those in the data structure of FIG. 16 and the reach points can besimilar to the reach scenario data structure above). In one exemplaryimplementation, identifying coordinates corresponding to reach points(e., reach points A through F, U through Z, etc.) can be created by theengine using the information in the table shown above an in FIG. 16.

FIG. 17 is a convex hull curve of the reach points in accordance withone embodiment.

FIG. 18 is a reach curve with human motion dynamics in accordance withone embodiment. In one exemplary implementation, the reach curve isconsistent with the following equation:

T=IQ+CQ

where T is torque, IQ are inertial forces and CQ are coriolis forces.

FIG. 19 is a reach curve with attitudinal control in accordance with oneembodiment. In one embodiment, the attitudinal control is associatedwith an arm and hand grabbing an object from a particular orientationduring an activity (e.g., engaging in an action, execution of a task,etc.). In one exemplary implementation, the curve is associated with aspecial case of basic motion dynamics with attitudinal controlrequirements.

FIG. 20 is a maximum reach curve associated with requirements ofdifferent activities in accordance with one embodiment. In oneembodiment, a maximum reach curve is created by overlaying a convex hullcurve, a reach curve with human motion dynamics, and reach curve withattitudinal control.

FIG. 21 is a flow chart of method 2100 in accordance with oneembodiment.

In block 2110, information associated with an activity space isaccessed. In one embodiment, the activity space includes a previouslyunmodeled entity. In one embodiment, accessing information includesinformation associated with sensing or monitoring an activity space.

In block 2120, the activity information is analyzed. In one embodiment,activity associated with the previously unmodeled entity is included inthe analysis.

In block 2130, feedback on the results of the analysis is forwarded. Inone embodiment, the feedback includes analysis results for the updatedmodeled information.

In block 2140, feedback is utilized in a coordinated plan check process.

In embodiment, an unmodeled object is detected in real time. In oneembodiment, paths are recomputed in real time. Activity spacereconfiguration to avoid issues with a previously unmodeled object canalso begin in real time. In one embodiment, a post-facto path recomputeprocess is run. Execution of the set up or reconfiguration can happenlater.

FIG. 22 is a flow chart of a coordinated plan check process 2200 inaccordance with one embodiment.

In block 2210 a solid/CAD model is created including updated modeledinformation.

In block 2220, an activity including the updated modeled information issimulated.

In block 2230 a coordinated path plan is generated for entities in theactivity space.

In block 2240, the coordinated path plan is tested.

FIG. 23 is a block diagram of an activity area 2305 and flow chart of aactivity modeling method in accordance with one embodiment. Activityarea 1105 includes actors 2321, 2322, 2323, and 2324, a product 2331 ona conveyor belt, and previously unmodeled entity 2341. In oneembodiment, previously unmodeled entity 2341 is block actor 2323 fromaccessing the product. This results in the coordinated path plan failingwhen there is an actual attempt to perform the activity. As shown, apreviously anticipated activity space version 2302 did not includepreviously unmodeled entity 2341. The information associated withpreviously anticipated activity space version 2302 is forwarded to thesolid/CAD models 2351, simulation 2352, and generated coordinated pathplan 2353. The coordinated path plan fails in block 2354 when an attemptto use is implemented

FIG. 24 is a block diagram of an activity area 2405 and flow chart of aactivity modeling method in accordance with one embodiment. Activityarea 2405 includes actors 2421, 2422, 2423, and 2424, a product 2431 ona conveyor belt, and previously unmodeled entity 2441. Sensor 2471senses previously unmodeled entity 2441 and forwards correspondingnon-modeled information 2472 to engine 2473. Engine 2473 (e.g., similarto engine 170, etc.) processes the information and generates updatedmodeled object information which is forwarded to the path process. Theinformation associated with the updated model information is forwardedto the solid/CAD models 2451, simulation 2452, and generated coordinatedpath plan 2453. The coordinated path plan is successful in block 2454when an attempt to use is implemented.

FIG. 25 is a block diagram of an activity area 2505 and flow chart of aactivity modeling method in accordance with one embodiment. Activityarea 2505 includes actors 2521, 2522, 2523, and 2524, a product 2531 ona conveyor belt, and previously unmodeled entity 2541. Sensor 2571senses previously unmodeled entity 2541 and forwards correspondingnon-modeled information 2572 to engine 2573. Engine 2573 (e.g., similarto engine 170, etc.) processes the information and generates updatedmodeled object information which is forwarded to the path process. Theinformation associated with the updated model information is forwardedto the solid/CAD models 2551, simulation 2552, and generated coordinatedpath plan 2553. The coordinated path plan is successful in block 2554when an attempt to use is implemented.

FIG. 26 is a block diagram of a data structure 2600 in accordance withone embodiment. In one exemplary implementation, data structure 2600 isa tree data structure. Data structure 2600 includes station informationnode 2610 which is a parent node for actor group information node 2620,body-in-white information node 2630, and tooling information node 2640.Actor group information node 2620 is a parent node for robot informationnode 2621, robot information node 2622, human information node 2623, andcobot information node 2624. Robot information node 2622 is a parentnode for base information node 2622 a, arm information node 2622 b, andend effector node 2622 c. Tooling information node 2640 is a parent nodefor base information node 2641, clamp node 2642, and pneumatic hoseinformation node 2643.

In one embodiment, a pneumatic nose is a newly discovered previouslyunmodeled entity and a data structure is automatically configured fornew information. FIG. 27 is a block diagram of a data structure 2700 inaccordance with one embodiment. In one exemplary implementation, datastructure 2700 is a tree data structure. Data structure 2700 includesstation information node 2710 which is a parent node for actor groupinformation node 2720, body-in-white information node 2730, and toolinginformation node 2740. Actor group information node 2720 is a parentnode for robot information node 2721, robot information node 2722, humaninformation node 2723, and cobot information node 2724. Robotinformation node 2722 is a parent node for base information node 2722 a,arm information node 2722 b, and end effector node 2722 c. Toolinginformation node 2740 is a parent node for base information node 2741,clamp node 2742, and pneumatic hose information node 2743.

The analysis can include an indication of a condition/situation. Thecondition/situation can be associated with various aspects of an entityand corresponding activity (e.g., a component, a part, a process step,etc.). The condition/situation can be an existing condition that hasbeen specifically sensed/detected. The analysis and correspondingcondition/situation can have a predictive aspect/nature (e.g., impendingcondition, future state, anticipated action, etc.).

In one exemplary implementation, a condition/situation corresponds toproper performance of an action (e.g., without defect, safely, etc.). Acondition/situation can be associated with a detrimental scenario (e.g.,a hazardous condition, a collision, overheating, a missed/skippedaction, a task performed incorrectly, a defective element, wrongcomponent used, a part not properly installed, etc.). In one embodiment,a prediction regarding movement can be made to determine if a dangerouscircumstance/collision is going to happen before a harmful impactoccurs. Feedback based on the analysis can be provided. The feedback canenable avoidance of detrimental conditions/situations.

FIG. 28 is a flow chart of a method 2800 in accordance with oneembodiment. In one embodiment, method 2800 is performed in real time. Itis appreciated other embodiments can be include post factooperations/elements.

In block 2810 information regarding an activity space is accessed inreal time. The information can be associated with a first actor in theactivity space and an object newly discovered in the activity space inreal time, (including sensed activity information). The information canbe accessed in real time. The newly discovered object can be apreviously non-modeled object (e.g., not previously modeled, etc.). Itis appreciated the newly discovered object can be various items (e.g., acomponent of a product, a second actor, a human. a device, etc.). Thework environment can be selected from a group of environmentscomprising; a manufacturing environment, a retail environment, a storagewarehouse environment. a medical environment. a food serviceenvironment, and a construction environment.

In block 2820, the activity information is automatically analyzed inreal time, including analyzing activity of the first actor with respectto the object. In one embodiment, the analyzing includes automaticconstruction of a simulation of the work environment in real time. Theanalyzing includes automatic identification of potential collisionpoints for the first actor in real time including potential collisionpoints with the newly discovered object. The analyzing includesautomatic identification of the newly discovered object. The results ofthe analysis can include a prediction of a future relationship of theactor and the object. The analyzing includes automatic construction of asimulation of the work environment in real time. The analyzing includesautomatic identification of potential collision points for the firstactor in real time including potential collision points with the newlydiscovered object.

In one embodiment, the analyzing includes overlaying spatial work/taskenvelope information. The overlaying can include temporal consideration.The analyzing can include identification of a working space associatedwith the first actor, and an indication of a detrimental activity by thefirst actor. The detrimental activity can be one predicted to result inan undesirable collision between the first actor and object.

In block 2830, feedback is forwarded in real time on the results of theanalysis to the first actor. In one embodiment, the analyzing andfeedback are provided in sufficient time to permit a change that avoidsthe detrimental activity. The respective feedback can include anobjective for the first actor or the second actor. The respectivefeedback can be a directive/control to change the first actor to avoid acollision (detrimental contact with each other).

FIG. 29 shows a block diagram of an example of a computing system 2900upon which one or more various embodiments described herein may beimplemented in accordance with various embodiments of the presentdisclosure. In various embodiments, the computer system 2900 may includea cloud-based computer system, a local computer system, or a hybridcomputer system that includes both local and remote devices. In a basicconfiguration, the system 2900 includes at least one processing unit2902 and memory 2904. This basic configuration is illustrated in FIG. 29by dashed line 2906. The system 2900 may also have additional featuresand/or functionality. For example, the system 2900 may include one ormore Graphics Processing Units (GPUs) 2910. Additionally, the system2900 may also include additional storage (e.g., removable and/ornon-removable) including, but not limited to, magnetic or optical disksor tape. Such additional storage is illustrated in FIG. 29 by removablestorage 2908 and non-removable storage 2920.

The system 2900 may also contain communications connection(s) 2922 thatallow the device to communicate with other devices, e.g., in a networkedenvironment using logical connections to one or more remote computers.Furthermore, the system 2900 may also include input device(s) 2924 suchas, but not limited to, a voice input device, touch input device,keyboard, mouse, pen, touch input display device, etc. In addition, thesystem 2900 may also include output device(s) 2926 such as, but notlimited to, a display device, speakers, printer, etc.

In the example of FIG. 29, the memory 2904 includes computer-readableinstructions, data structures, program modules, and the like associatedwith one or more various embodiments 2950 in accordance with the presentdisclosure. However, the embodiment(s) 2950 may instead reside in anyone of the computer storage media used by the system 2900, or may bedistributed over some combination of the computer storage media, or maybe distributed over some combination of networked computers, but is notlimited to such.

It is noted that the computing system 2900 may not include all of theelements illustrated by FIG. 29. Moreover, the computing system 2900 canbe implemented to include one or more elements not illustrated by FIG.29. It is pointed out that the computing system 2900 can be utilized orimplemented in any manner similar to that described and/or shown by thepresent disclosure, but is not limited to such.

Some portions of the detailed descriptions are presented in terms ofprocedures, logic blocks, processing, and other symbolic representationsof operations on data bits within a computer memory. These descriptionsand representations are the means generally used by those skilled indata processing arts to effectively convey the substance of their workto others skilled in the art. A procedure, logic block, process, etc.,is here, and generally, conceived to be a self-consistent sequence ofsteps or instructions leading to a desired result. The steps includephysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical, magnetic,optical, or quantum signals capable of being stored, transferred,combined, compared, and otherwise manipulated in a computer system. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare associated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. Unless specificallystated otherwise as apparent from the following discussions, it isappreciated that throughout the present application, discussionsutilizing terms such as “processing”, “computing”, “calculating”,“determining”, “displaying” or the like, refer to the action andprocesses of a computer system, or similar processing device (e.g., anelectrical, optical or quantum computing device) that manipulates andtransforms data represented as physical (e.g., electronic) quantities.The terms refer to actions and processes of the processing devices thatmanipulate or transform physical quantities within a computer system'scomponent (e.g., registers, memories, other such information storage,transmission or display devices, etc.) into other data similarlyrepresented as physical quantities within other comonents.

The foregoing descriptions of specific embodiments of the presenttechnology have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise forms disclosed, and obviously manymodifications and variations are possible in light of the aboveteaching. The embodiments were chosen and described in order to bestexplain the principles of the present technology and its practicalapplication, to thereby enable others skilled in the art to best utilizethe present technology and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

It is appreciated that methods and processes described herein can beperformed, in whole or in part, by an engine (e.g., 170, 860, 970,etc.). The methods and processes can be performed in whole or in part inreal time, post-facto, on-demand, or some combination thereof, and soon.

What is claimed is:
 1. A method comprising: accessing informationassociated with an activity space, including information on a newlydiscovered previously unmodeled entity; analyzing the activityinformation, including activity information associated with thepreviously unmodeled entity; forwarding feedback on the results of theanalysis, including analysis results for the updated modeledinformation; and utilizing the feedback in a coordinated path plan checkprocess.
 2. The method of claim 1, wherein the coordinated path plancheck process comprises: creating a solid/CAD model including updatedmodeled information; simulating an activity including the updatedmodeled information; generating a coordinated path plan for entities inthe activity space; and testing the coordinated path plan.
 3. The methodof claim 1, wherein the coordinated path plan check process is a success4. The method of claim 1, wherein the analyzing includes automaticidentification of potential collision points for a first actor,including potential collision points with the newly discovered object.5. The method of claim 1, wherein the newly discovered previouslyunmodeled entity interferes with an actor from performing an activity.6. The method of claim 1, wherein the newly discovered object is aportion of a tool component of a product.
 7. A system comprising: one ormore data storage units, the one or more data storage units configuredto store information for the engine, including information associatedwith one or more stations, wherein the one or more stations include aplurality of entities, wherein the plurality of entities includes afirst actor and a second actor; one or more engines configured to:access information associated with an activity space, includinginformation on a newly discovered previously unmodeled entity; analyzethe activity information, including activity information associated withthe previously unmodeled entity; forward feedback on the results of theanalysis, including analysis results for the updated modeledinformation; and utilize the feedback in a coordinated path plan checkprocess.
 8. The system of claim 7, wherein the coordinated path plancheck process comprises: creating a solid/CAD model including updatedmodeled information; simulating an activity including the updatedmodeled information; generating a coordinated path plan for entities inthe activity space; and testing the coordinated path plan.
 9. The systemof claim 7, wherein the coordinated path plan check process is a success10. The system of claim 7, wherein the analyzing includes automaticidentification of potential collision points for a first actor,including potential collision points with the newly discovered object.11. The system of claim 7, wherein the newly discovered previouslyunmodeled entity interferes with an actor from performing an activity.12. The system of claim 7, wherein the newly discovered object is aportion of a tool component of a product.
 13. The system of claim 7, theengine processes the information and generates updated modeled objectinformation which is forwarded to the path process.
 14. One or morenon-transitory computing device-readable storage mediums storinginstructions executable by one or more computing devices to perform amethod comprising: accessing information associated with an activityspace, including information on a newly discovered previously unmodeledentity; analyzing the activity information, including activityinformation associated with the previously unmodeled entity; forwardingfeedback on the results of the analysis, including analysis results forthe updated modeled information; and utilizing the feedback in acoordinated path plan check process.
 15. The device-readable storagemediums of claim 14, wherein the coordinated path plan check processcomprises: creating a solid/CAD model including updated modeledinformation; simulating an activity including the updated modeledinformation; generating a coordinated path plan for entities in theactivity space; and testing the coordinated path plan.
 16. Thedevice-readable storage mediums of claim 14, wherein the coordinatedpath plan check process is a success
 17. The device-readable storagemediums of claim 14, wherein the analyzing includes automaticidentification of potential collision points for a first actor,including potential collision points with the newly discovered object.18. The device-readable storage mediums of claim 14, wherein the newlydiscovered previously unmodeled entity interferes with an actor fromperforming an activity.
 19. The device-readable storage mediums f claim14, wherein the newly discovered object is a portion of a tool componentof a product.